The Effect of Semantic Knowledge Expansion to Textual Entailment Recognition

This paper studies the effect of semantic knowledge expansion applied to the Textual Entailment Recognition task. In comparison to the already existing approaches we introduce a new set of similarity measures that captures hidden semantic relations among different syntactic categories in a sentence. The focus of our study is also centred on the synonym, antonym and verb entailment expansion of the initially generated pairs of words. The main objective for the realized expansion concerns the finding, the affirmation and the enlargement of the knowledge information. In addition, we applied Latent Semantic Analysis and the cosine measure to tune and improve the obtained relations. We conducted an exhaustive experimental study to evaluate the impact of the proposed new similarity relations for Textual Entailment Recognition.